Non-statistical method for compressing and decompressing complex SAR data

ABSTRACT

Provided is a non-statistical method for compressing and decompressing complex SAR data derived from reflected energy. The method includes selecting a first FFT to provide a target ratio of pixel spacing to resolution. A second FFT is then selected which is smaller than the first FFT. The data is zero-padded to fill the second FFT and transformed to provide at least one transfer frequency. This transfer frequency is then transferred to the at least one remote site. At the remote site the second FFT is inverted to restore the data from the received transfer frequency. The restored data is then zero-padded again to fill the first FFT. The first FFT is then used to transform the zero-padded restored data to provide a data set of points with the target ratio of pixel spacing to resolution.

FIELD

This invention relates generally to the field of synthetic apertureradar (SAR), and more specifically to the transferring of processedcomplex data from a location (such as an aircraft or satellite) to theground over a data link.

BACKGROUND

In general, the larger an antenna, the greater the amount of informationthe antenna can obtain about a viewed object or area, and the moreinformation the antenna obtains, the finer the resolution and moreuseful the imaging data becomes. Very large antennas are prohibitivelyexpensive to place in orbit or on aircraft; however, researchers havelearned to combine motion of a relatively small antenna with advancedsignal processing techniques to simulate the results otherwiseobtainable from only a very large antenna.

Synthetic Aperture Radar (SAR) antennas generate rapid radar pulses toimage surface areas over which an aircraft or satellite carrying the SARantenna is passing. The backscatter responses from the radar pulses arereceived by the SAR antenna and are interpreted with respect to phaseand amplitude and recorded over a measured distance. By combining thebackscatter responses from many pulses as the SAR passes overhead, asynthetic aperture is formed that is far larger than the aperture of theactual physical SAR antenna.

The SAR data is collected by the radar antenna as radio frequency analogdata and converted to digital format through an analog-to-digitalconverter and provided to an image formation processor. The raw,unprocessed data is commonly referred to as Video Phase History (VPH)data. Each block of VPH data includes two components, an In-phase (I)component and a Quadrature (Q) component. As the VPH data is a waveform,the I is the wave's real component and the Q the imaginary component,with the I and Q combining to provide the wave's overall phase andmagnitude.

The output of the image formation processor is detected image data whichcan be displayed as is, and complex image data which can be used asinput to Coherent Change Detection and other complex SAR exploitationtools. This output data is transferred by a data link (e.g., radiofrequency communications link) from the aircraft to the ground, where aground based complex data exploitation processor at the ground stationrenders detected image or other information on a display.

The ultimate picture provided by SAR is composed of pixels—the smallestunits of the picture composition. Not surprisingly, each pixel istherefore represented by the complex image data, and more specificallytwo data channels that deliver the phase and the magnitude components ofthe complex image. In addition, for each pixel of the complex image datadisplayed there is an associated Range and Azimuth corresponding to thephysical location or facet scanned.

Advances in SAR technology have enabled the SAR sensors to collectincreasingly large amounts of complex image data. The transmission ofsuch data to ground stations typically involves a radio transmissionhaving a limited frequency bandwidth. Moreover, the steady increase incollection efficiency and image size has raised the total data volume tothe point where data latency is a problem and a limiting factor in thedistribution of near real time information. When the imaged areacorresponds to a disaster area, such as a coast line ravaged by atsunami, or a combat zone, real time transfer and processing is key tothe prevention of loss of life and property.

More specifically, at the present time each SAR pixel represented incomplex image data is 64 bits, 32 bits each for the I and Q components.In certain SAR configurations, this information is encoded as phase andlogarithm of magnitude with two hundred fifty six levels assigned toeach. As a typical SAR image may include billions of pixels, the volumeof data collected for transmission and processing is enormous. TheUnited States Government has been funding investigations to solve thebandwidth and latency problems by focusing on compressing thetransmitted pixels to a target of 5 bits per pixel without incurringimage degradation.

Whereas, phase information of the image had been previously discarded,resulting in an immediate reduction in data by a factor of two, thenewly recognized importance of phase data necessitates that it too bemaintained and transmitted. Previous attempts to compress complex SARdata without degrading the information have not succeeded. Existingcompression algorithms, such as JPEG, were specifically designed to takeadvantage of optical data statistics and high local data correlation.

More specifically, JPEG and other compression algorithms rely onstatistical repetition of data, and the ability to represent astatistical group of pixels with a token representative. For example theimage of a black ball on a red floor may be highly compressed because ofthe highly statistical nature of red and black elements within theimage. If the red floor accounts for 95% of the image, a compression ofwell over 75:1 is easily achieved as for each red pixel there is a highlocal correlation to other red pixels. However, where the image data isperhaps best described as nearly white noise, and statistical elementsare rare if even identifiable, the compression method falters as thestatistical component is not substantially present.

Magnitude images generated from SAR Magnitude data do compress as themagnitude data generally has correlation. However, Phase data is highlyuncorrelated and does not compress well using existing techniques.Several compression methods have been proposed to compress SAR data.While they generally work quite well for the Magnitude data, they do notefficiently compress the Phase data, because the compression methods,designed for visible electro optical imagery, rely on high local datacorrelation to achieve good compression results. When the data is leftin I and Q form, neither component will compress well with JPEG typealgorithms.

Complex SAR image data, either in I, Q or phase, magnitude form do notprovide high rates of local data correlation. More simply stated,complex SAR data are highly non-statistical, and as such can not beeffectively processed by statistical compression methods.

While various compression methods have been explored, each tends toapply unequally to either the magnitude or the phase component, whichlimits the later accuracy and usage of the compressed data. Rememberingthat the SAR gathering device is typically in an aircraft or satellite,the compression method employed also should involve minimalcomputational complexity so as to reduce the processing requirementsavailable at the point of collection.

Hence, there is a need for a complex SAR data compression method topermit improved transfer from the gathering location to the imageproduction location that overcomes one or more of the technical problemsfound in existing complex SAR data compression methods.

SUMMARY

This invention provides a non-statistical method for compressing anddecompressing complex SAR data derived from a reflected energy.

In particular, and by way of example only, according to one embodimentof the present invention, a non-statistical method for compressing anddecompressing complex SAR data derived from a reflected energy,including: selecting a first Fast Fourier Transforms (FFT) to provide atarget ratio of pixel spacing to resolution; selecting a second FFTsmaller than the first FFT; zero-padding the data to fill the secondFFT; transforming the zero-padded data with the second FFT to provide atleast one transfer frequency; transferring the at least one transferfrequency to a remote location; inverting the second FFT transformationat the remote location to restore the data from the transferred at leastone transfer frequency as restored data; zero-padding the restored datato fill the first FFT; and transforming the zero-padded restored data toprovide a data set of points with the target ratio of pixel spacing toresolution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a simplified diagram of an SAR system which may ormay not incorporate an embodiment of the present invention;

FIG. 2 is a flow diagram of an SAR system incorporating an embodiment ofthe present invention;

FIG. 3 is a schematic illustration of different FFTs and the resultingwave forms;

FIG. 4 is a more specific flow diagram of the non-statistical method forcompressing and decompressing complex SAR data in at least oneembodiment; and

FIG. 5 is a conceptual block diagram of a non-statistical method forcompressing and decompressing complex SAR data in accordance with anembodiment.

DETAILED DESCRIPTION

Before proceeding with the detailed description, it is to be appreciatedthat the present teaching is by way of example only, not by limitation.The concepts herein are not limited to use or application with aspecific method of compressing and decompressing complex SAR data. Thus,although the instrumentalities described herein are for the convenienceof explanation, shown and described with respect to exemplaryembodiments, it will be appreciated that the principles herein may beapplied equally in other types of methods involving the non-statisticalcompression and decompression of data.

FIGS. 1 and 2 illustrate an exemplary SAR system incorporating anon-statistical system for compressing and decompressing complex SARdata, in accordance with at least one embodiment. More specifically,FIG. 1 provides an overview of the SAR system 100, and FIG. 2 providesan overview of a non-statistical method for compressing anddecompressing complex SAR data, hereinafter “SAR compression system”200, in accordance with at least one embodiment. As SAR compressionsystem 200 is generally incorporated as a preferred component of an SARsystem 100, discussion in context of the overall SAR system 100 isbelieved helpful.

With respect to FIG. 1, an SAR equipped plane 102 is following a knownflight path 104 over the ground 106. The SAR antenna radar beam 108collects images of the ground surface across swath 110 as the planefollows flight path 104. Collected image data is sent to the on boardprocessor where processing of the data is further performed so as toprovide complex visual image data 112. The data 112 is then sent fromthe plane 102 to a ground station 114. The resolution of the SAR systempermits a scanned target 116 on the surface to be recognized andenlarged upon a visual display 118. For each element of image datacollected from the surface 106, there is an associated range direction120 and azimuth direction 122 upon the surface 106.

Shown within dotted section 122 is a simplified overview of the airborneSAR data processing operations. SAR data is collected by an antenna 124as radio frequency “RF” analog data, received by an RF receiver 126, andconverted from an intermediate frequency “IF” to digital format throughan analog-to-digital converter 128. A pulse buffer 130 may also beemployed to insure proper delivery of the signal to the airborne imageformation processor 132.

The raw, unprocessed data is commonly referred to as Video Phase History(VPH) data. Each block of VPH data includes two components, an In-phase(I) component and a Quadrature (Q) component. As the VPH data is awaveform, the I is the wave's real component and the Q the imaginarycomponent, with the I and Q combining to provide the wave's overallphase and magnitude. The data output from the image formation processoris a floating point sixty-four bit value for each sample, withthirty-two bits for I and thirty-two bits for Q.

Magnitude (M) and Phase (Φ) components are obtained from the I and Qcomponents by the following equations:M=(I ² +Q ²)^(1/2)Φ=tan⁻¹(Q/I)

Similarly, I and Q components are obtained from the Magnitude and Phasecomponents by the following equations:I=M cos ΦQ=M sin Φ

The output of the airborne image formation processor 132 is detectedimage data which can be displayed as is, and complex image data whichcan be used as input to Coherent Change Detection and other complex SARexploitation tools. This output data is transferred by a data link(e.g., radio frequency communications link) from the aircraft to theground 106, where a ground based complex data exploitation processor atthe ground station 114 renders detected image or other information on adisplay 118.

The ultimate picture provided by SAR is composed of pixels—the smallestunits of the picture composition. Not surprisingly, each pixel istherefore represented by the complex image data, and more specificallytwo data channels that deliver the phase and the magnitude components ofthe complex image. In addition, for each pixel of the complex image datadisplayed there is an associated Range and Azimuth corresponding to thephysical location or facet scanned.

SAR radar is side looking radar, i.e., the map is always of the groundto the left or right of the ground below the aircraft 102. This isfurther illustrated in the small inset 150 provided with FIG. 1. Rangeis the distance from the radar to the centerline of the area to bemapped, and Azimuth is the angle between the SAR antenna and the pointon the scanned surface from which the pixel data is being collected(forward or backward from the center line). In other words, locationsparallel to the antenna flight path are either increasing or decreasingin Azimuth. Locations running perpendicular to the antenna flight pathare either increasing or decreasing in Range.

With respect to the general overview provided in FIG. 1, FIG. 2 nowillustrates a conceptual SAR system 202 incorporating a non-statisticalSAR compression system 200, in accordance with at least one embodiment.More specifically, an antenna 204 collects reflected energy 206 in theform of radio frequency analog data, which is in turn received by an RFreceiver 208 and converted to a digital format through ananalog-to-digital converter 210. A pulse buffer 212 may also be providedto insure proper delivery of the digital signal to the airborne imageformation processor 214.

The raw, unprocessed data is the VPH data discussed above, consisting ofan In-phase (I) component and a Quadrature (Q) component. The dataoutput from the image formation processor is a floating point sixty-fourbit value for each sample, with thirty-two bits for I and thirty-twobits for Q.

Stated simply, SAR system 202 extrapolates image data from the constantchange in the Phase and Magnitude of the reflected energy beam processedas VPH data, as it tracks from one ground location to another groundlocation. The change in Phase and Magnitude is a function of the rangeand azimuth which are of course different for each location.

SAR compression system 200 advantageously permits transfer of the imagedata output from the image formation processor 214, located in anairborne platform, to one or more ground based sites over a radiofrequency data link in less than sixty-four bits per sample, and withoutloss of desired image data. SAR compression system 200 may be anincorporated component of the image formation processor 214, however ithas been illustrated separately in block component form for ease ofdiscussion and illustration.

More specifically, the SAR compression system 200 receives data from theimage formation processor, as shown in block 216. A target ratio ofpixel spacing to resolution the resulting desired image is also known asa pre-determined element. In at least one embodiment, it is desirable tohave the pixel spacing set to a distance closer than the resolution, assuch close spacing permits enhanced distinction between multiple targetelements in the resulting image. In at least one embodiment, this targetratio is 0.56.

Fast Fourier Transforms (FFTs) are well known and frequently utilizedalgorithms applied to compute Discrete Fourier Transforms (DFTs) andtheir inverses, as FFTs typically involve a fraction of the mathematicaloperations required with DFTs. With digital signal processing, FFTs areextremely useful in permitting the representation of I & Q values asfrequency. As FFTs generally require the use of data sets in which thenumber of samples is equal to an integer power of two, the actual dataset is zero-padded to fill a selected FFT matrix as is understood in theart.

Zero-padding directly affects the resolution of the FFT output points,which in the case of the SAR system correspond to spacing and frequencyof the output points. Simply stated, the more zeros added, and thus thelarger the FFT matrix, the smaller the resolution of the output points.Although zero-padding the data for an appropriately sized FFT isrequired to provide the desired resolution in the resulting SAR images,the use of such an appropriately sized FFT is not required for otherprocessing and computational steps.

As is shown in block 218, a first FFT is selected to provide the desiredtarget ratio of pixel spacing to resolution. A second FFT is thenselected, the second FFT being smaller than the first. FFT, as shown inblock 220. The data is then zero-padded to fill the second FFT, as shownin block 222.

The data is then transformed as shown in block 224 to provide a transferfrequency. More specifically, the data is encoded through the second FFTto provide a plurality of pixels, each pixel determined from a Range andAzimuth location set corresponding to a physical location. The Range andAzimuth location set correspond to the Phase and Magnitude of thereflected energy corresponding to the actual physical location. Eachpixel is represented as a sixty-four bit value, thirty-two bits for thelog of the Magnitude and thirty-two bits for the arctangent of thePhase.

A further transformation is performed to convert each floating pointthirty-two bit value (LogMag and ArcTan Phase) to a fixed 8 bit value(described in greater detail below and shown in FIG. 4). The combinedfixed sixteen bit value (consisting of the fixed eight bit LogMag andfixed eight bit ArcTan Phase), is then transferred as a digital transferfrequency from the airborne location to one or more ground locations, asshown in block 226 and transmission arc 228.

Upon receipt at a ground station, the second FFT is inverted so as torestore the SAR data, as shown in block 230. The restored data is thenzero-padded so as to fill the first FFT, as shown in block 232. With thefirst FFT now filled, the zero-padded data is transformed so as toprovide a data set of points with the desired target ratio of pixelspacing to resolution, as shown in block 234. The process performed inblock 234 may be integrated with a ground based complex dataexploitation processor 236, however it has been shown as a separateelement for ease of discussion. The output from such a ground basedcomplex data exploitation processor 236 is provided to a display 238 soas to render the desired SAR image.

As stated above, the SAR compression system 200 incorporates the use oftwo different sized FFTs. As there is a difference in the zero-paddingnecessary to fill the different FFT's the resulting output from each isrelated. FIG. 3 conceptually illustrates a first FFT (FFT#1) and secondFFT (FFT#2)each of which has been provided with the same data set (A, B,C, D), and the resulting frequency output. The output from second FFT isinsufficient to provide image resolution as desired. However, as the FFTtransformation processes may be inverted to substantially restore theinitial data (A, B, C, D), the essential data is not lost.

The advantageous ability to use the smaller second FFT is due to thetarget ratio of pixel spacing to resolution. The final pixel spacingdetermines the amount of zero fill used in the FFTs. The high ratio ofzero fill used for the final desired pixel spacing implies that the sincfunction produced by the FFT from each sinusoidal input component iseffectively sampled at significantly higher than the Nyquist ratio.Doing the smaller FFT with less zero fill produces from the samesinusoidal input a sinc function with fewer samples per cycle but stillenough to define the sinc function unambiguously, as shown by example inFIG. 3. This makes it possible to perform inverse FFTs and reconstructthe original sinusoid with no loss of information.

In at least one embodiment, wherein the desired target ratio is 0.56,the first FFT is a 16K FFT, and the second FFT is 8K FFT, or half thesize of first FFT. FIG. 4 provides a more detailed overview of SARcompression system 200 incorporating these first and second FFTs in atleast one embodiment. The data is provided in a two dimensional tablecorresponding to Range and Azimuth of a location on the ground. It isappreciated that the described method need not be performed in the orderin which it is described, but that this description is merely exemplaryof at least one method of using SAR compression system 200.

A transformation is performed to determine the Range (RG) as a locationX, shown in block 400 and to determine the Azimuth (Az) as a location Y,shown in block 402. Phase data is processed by computing: Φ=tan⁻¹ (Y/X),as shown in block 404. The resulting thirty-two bit floating point valueis then converted to an eight-bit fixed value as shown in block 406.

The Log of the Magnitude is also determined by computing:log(X²+Y²)^(1/2), as shown in block 408. This resulting thirty-two bitfloating point value is also converted to an eight-bit fixed value asshown in block 410. The initial thirty-two bit floating point value andthe converted eight-bit value for the LogMag are highly statistical andas such, traditional encoding methodologies based on statistics areapplicable. In at least one embodiment an optional Huffman Coder isincluded in the system as indicated in dotted block 412. The Huffmancoding process is an entirely lossless process which may be performed toreduce the fixed eight-bit value to a five or six -bit value dependingon the statistics of the Log magnitude data.

The computations of LogMag and ArcTan of Phase may be performedsequentially, or as substantially contemporaneous operations. Uponencoding transformation to the two eight bit values, the values aretransferred, i.e., transmitted, as digital information from the airborneSAR gathering station to one or more ground locations, indicated bydotted arrows. Moreover, the actions performed within dotted boundary414 are airborne activities, the remaining actions within dottedboundary 416 are ground based activities.

Upon receipt at the ground reception location, a lookup table isutilized to translate the fixed eight-bit values back to floating pointthirty two-bit values, as shown in blocks 418, 420. These floating pointvalues are then combined, as shown in block 422.

The second FFT is then inverted (IFFT) so as to restore the initialRange and Azimuth data as shown in blocks 424, 426. Following therecovery of the Azimuth and Range data, the recovered data is once againtransformed, this time by the first FFT so as to provide data points forRange and Azimuth having the desired target ratio of pixel spacing toresolution, as shown in blocks 428, 430.

Moreover SAR compression system 200 is substantially lossless in termsof SAR image data, yet it advantageously compresses the data with anon-statistical method so as to utilize less radio frequency bandwidthfor the transfer of the digital information from the airborne collectionsite to the one or more ground sites. As such the data may betransferred much more quickly so as to permit ground based imageformation that is near real time with the airborne data collection.

Having described embodiments of the method, FIG. 5 conceptuallyillustrates a block diagram of the computer program architecture of SARcompression system 200 in accordance with at least one embodiment. SARcompression system 200 may be implemented on a computer having typicalcomputer components such as a processor, memory, storage devices andinput and output devices.

During operation, SAR compression system 200 may be maintained in activememory for enhanced speed and efficiency. As suggested above in thediscussion of airborne processes and ground processes, SAR compressionsystem 200 is generally operated on two or more physically separatecomputer systems. In addition each computer system may be part of acomputer network and thus may utilize distributed resources.

SAR compression system 200 has two basic components, a compressionroutine 500 and a decompression routine 502. Generally, the compressionroutine 500 is to be considered the airborne routine and thedecompression routine 502 is to be considered the ground based routine.It is of course understood and appreciated that the compression routinemay be implemented onboard a satellite in space, thus being space borne.It is further understood and appreciated that the compression routine500 may be implemented by a ground based processing system that isacting to distribute the compressed information to other ground basedlocations.

As shown, in at least one embodiment, the compression routine 500 has aninput routine 504, a selection routine 506, a transformation routine 508and an output routine 510. The decompression routine 502 has an inputroutine 512, an invert transform routine 514 and an image processingroutine 516. It is understood and appreciated that these routines may befurther subdivided and or combined in particular applications anddifferent embodiments.

With respect to the compression routine, the input routine isoperatively associated with at least one input device for receiving thecomplex SAR data as described above with respect to FIG. 2. In at leastone embodiment this SAR data is provided in the form of Video PhaseHistory (VPH) data. Such VPH data may be provided directly by the imageformation processor component 214 as shown in FIG. 2. In addition, asystem user also provides the target ratio of pixel spacing toresolution. Additional information such as time, date and location mayalso be provided so as to later identify the resulting SAR image data.

To achieve the provided target resolution, the selection routine 506selects a first FFT and then a second FFT smaller than the first FFT. Inat least one embodiment, the second FFT is half the size of the firstFFT. The transformation routine 508 is operable to zero-pad the VPH datato fill the second FFT, and perform the transformation as describedabove to provide at least one transfer frequency. The output routine 510is operatively coupled with an output device to provide at least thetransfer frequency. Moreover, typically the output routine 510 iscoupled to a transponder for a radio frequency data link.

With respect to the decompression routine 502, the input routine 512 isoperatively associated with at least one input device for receiving theat least one transfer frequency transmitted by the compression routine.Moreover, typically the input routine 512 is coupled to a transceiverfor a radio frequency data link. The decompression routine 502 furtherincludes an invert transformation routine for restoring the VPH datafrom the transferred transfer frequency. More specifically the inverttransformation routine 514 inverts the second FFT transformation of thedata. An image processing routine 516 transforms the restored data withthe first FFT to provide a data set of points with the desired targetratio of pixel spacing to resolution.

As performed in a computer environment, the SAR compression system 200may be rendered in a variety of different forms of code and instructionas may be preferred for different computer systems and environments. Thecomputer system may be a commercially available system, such as adesktop workstation unit provided by IBM, Dell Computers, Gateway,Apple, Sun Micro Systems, or other computer system providers, or aproprietary system.

In addition, the computer system may also be a networked computersystem, wherein memory storage components such as hard drives andadditional CPUs and output devices are provided by physically separatecomputer systems tied together within the network. Indeed the airborneand ground based computer systems implementing SAR compression system200 may be part of the same computer network.

Those skilled in the art will select an appropriate computer readablemedium upon which SAR compression system 200 is provided for use in adesired computer system. Moreover, SAR compression system 200 may beprovided by CD Rom, EPROM, magnetic tape, flash memory, or othercomputer readable medium compatible with the computer system enabled toperform SAR data collection and processing.

Changes may be made in the above methods, systems and structures withoutdeparting from the scope hereof. It should thus be noted that the mattercontained in the above description and/or shown in the accompanyingdrawings should be interpreted as illustrative and not in a limitingsense. The following claims are intended to cover all generic andspecific features described herein, as well as all statements of thescope of the present method, system and structure, which, as a matter oflanguage, might be said to fall therebetween.

1. A non-statistical method for compressing and decompressing complexSAR data derived from a reflected energy, comprising: selecting a firstFFT to provide a target ratio of pixel spacing to resolution; selectinga second FFT smaller than the first FFT; zero-padding the data to fillthe second FFT; transforming the zero-padded data with the second FFT toprovide at least one transfer frequency; transferring the at least onetransfer frequency to a remote location; inverting the second FFTtransformation at the remote location to restore the data from thetransferred at least one transfer frequency as restored data;zero-padding the restored data to fill the first FFT; and transformingthe zero-padded restored data to provide a data set of points with thetarget ratio of pixel spacing to resolution.
 2. The method of claim 1,wherein the target ratio is between approximately 0.3 and 0.7
 3. Themethod of claim 2, wherein the target ratio is approximately 0.56. 4.The method of claim 1, wherein the complex SAR data is collected throughan oversampling process.
 5. The method of claim 1, wherein the secondFFT is half the size of the first FFT.
 6. The method of claim 1, whereintransforming the zero-padded data with the second FFT is performed toprovide a Range location and an Azimuth location.
 7. The method of claim6, wherein for each Range and Azimuth location set there is anassociated Phase of the reflected energy, further including determiningfor each Range and Azimuth location set the arctangent of the Phase as afirst floating point value.
 8. The method of claim 7, wherein the firstfloating point value is converted to a first fixed bit value beforetransferring to the remote site.
 9. The method of claim 8, wherein theremote site utilizes a look up table to restore the first floating pointvalue from the first fixed bit value.
 10. The method of claim 6, whereinfor each Range and Azimuth location set there is an associated Magnitudeof the reflected energy, further including determining for each Rangeand Azimuth location set a logarithm of the Magnitude as a secondfloating point value.
 11. The method of claim 10, wherein the secondfloating point value is converted to a second fixed bit value beforetransferring to the remote site.
 12. The method of claim 11, wherein aHuffman Coder further reduces the size of the second fixed bit value.13. The method of claim 11, wherein the remote site utilizes a look uptable to restore the second floating point value from the second fixedbit value.
 14. The method of claim 1, wherein the method is stored on acomputer-readable medium as a computer program, which when executed by acomputer will perform the steps of non-statistically compressing anddecompression complex SAR data.
 15. A non-statistical system forcompressing and decompressing complex SAR data derived from a reflectedenergy, comprising: receiving means for receiving complex SAR datacollected through an oversampling process; selecting means for selectinga first FFT to provide a target ration of pixel spacing to resolutionand a second FFT smaller the size of the first FFT; first transformingmeans for zero-padding the data to fill the second FFT and transform thezero-padded data with the second FFT to provide at least one transferfrequency; transfer means for transferring the at least one transferfrequency to a remote location; inverting means for inverting the secondFFT transformation at the remote location to restore the data from thetransferred at least one transfer frequency as restored data; and secondtransforming means for zero-padding the restored data to fill the firstFFT and transforming the zero-padded restored data to provide a data setof points with the target ratio of pixel spacing to resolution.
 16. Acomputer-readable medium on which is stored a computer program for anon-statistical method for compressing and decompressing complex SARdata derived from a reflected energy, the computer program comprisinginstructions which, when executed by a computer, perform the steps of:selecting a first FFT to provide a target ratio of pixel spacing toresolution; selecting a second FFT smaller than the first FFT;zero-padding the data to fill the second FFT; transforming thezero-padded data with the second FFT to provide at least one transferfrequency; transferring the at least one transfer frequency to a remotelocation; inverting the second FFT transformation at the remote locationto restore the data from the transferred at least one transfer frequencyas restored data; zero-padding the restored data to fill the first FFT;and transforming the zero-padded restored data to provide a data set ofpoints with the target ratio of pixel spacing to resolution.